In this work, we propose a diffusion-based model that generates realistic and long-form dance sequences conditioned on music.
We evaluate our model on multiple automated metrics (including our proposed PFC) and a large user study, and find that it achieves state-of-the-art results on the AIST++ dataset and generalizes well to in-the-wild music inputs.
Importantly, we demonstrate that our model admits powerful editing capabilities, allowing users to freely specify both temporal and joint-wise constraints.
The introduction of editable dance generation provides multiple promising avenues for future work.
Whereas our method is able to create arbitrarily long dance sequences via the chaining of locally consistent, shorter clips, it cannot generate choreographies with very-long-term dependencies. Future work may explore the use of non-uniform sampling patterns such as those implemented in Harvey et al. [1]}, or a variation of the frame inbetweening scheme used in Ho et al. [2]}.
Editability also opens the door to the generation of more complex choreographies, including multi-person and scene-aware dance forms.
We are excited to see the future that this new direction of editable dance generation will enable.
